融合视觉与传感的智能灌溉系统，节水三成以上
An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control
- 用轻量级YOLO+多传感器融合实现植物定位与精准控制
- 在三种场景下节水30%-50%，水利用效率超92%
- 适合农业机器人、智慧温室等需要自适应灌溉的场景
本文提出一种智能节水灌溉系统，应对精准农业中水资源利用效率低、地形适应性差的问题。系统通过多传感器融合集成计算机视觉、机器人控制与实时稳定技术。部署在K210嵌入式视觉处理器上的轻量级YOLO模型，在不同光照条件下实现超过96%的植株容器检测准确率。针对手持相机机械臂结构设计的简化手眼标定算法，使末端执行器定位成功率超过90%。基于STM32F103ZET6主控芯片与JY901S惯性测量数据的主动调平系统，可在10度坡度上实现1.8秒响应时间的平台稳定。三个模拟农业环境（标准温室、丘陵地形、复杂光照）的实验表明，相比传统漫灌，用水量减少30%-50%，所有测试场景水利用效率均超过92%。
原文摘要 · Abstract (English)
This paper introduces an intelligent water-saving irrigation system designed to address critical challenges in precision agriculture, such as inefficient water use and poor terrain adaptability. The system integrates advanced computer vision, robotic control, and real-time stabilization technologies via a multi-sensor fusion approach. A lightweight YOLO model, deployed on an embedded vision processor (K210), enables real-time plant container detection with over 96% accuracy under varying lighting conditions. A simplified hand-eye calibration algorithm-designed for 'handheld camera' robot arm configurations-ensures that the end effector can be precisely positioned, with a success rate exceeding 90%. The active leveling system, driven by the STM32F103ZET6 main control chip and JY901S inertial measurement data, can stabilize the irrigation platform on slopes up to 10 degrees, with a response time of 1.8 seconds. Experimental results across three simulated agricultural environments (standard greenhouse, hilly terrain, complex lighting) demonstrate a 30-50% reduction in water consumption compared to conventional flood irrigation, with water use efficiency exceeding 92% in all test cases.
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